Advanced Pre-Processing and Change-Detection Techniques for the Analysis of Multitemporal VHR Remote Sensing Images

Remote sensing images regularly acquired by satellite over the same geographical areas (multitemporal images) provide very important information on the land cover dynamic. In the last years the ever increasing availability of multitemporal very high geometrical resolution (VHR) remote sensing images (which have sub-metric resolution) resulted in new potentially relevant applications related to environmental monitoring and land cover control and management. The most of these applications are associated with the analysis of dynamic phenomena (both anthropic and non anthropic) that occur at different scales and result in changes on the Earth surface. In this context, in order to adequately exploit the huge amount of data acquired by remote sensing satellites, it is mandatory to develop unsupervised and automatic techniques for an efficient and effective analysis of such kind of multitemporal data. In the literature several techniques have been developed for the automatic analysis of multitemporal medium/high resolution data. However these techniques do not result effective when dealing with VHR images. The main reasons consist in their inability both to exploit the high geometrical detail content of VHR data and to model the multiscale nature of the scene (and therefore of possible changes). In this framework it is important to develop unsupervised change-detection(CD) methods able to automatically manage the large amount of information of VHR data, without the need of any prior information on the area under investigation. Even if these methods usually identify only the presence/absence of changes without giving information about the kind of change occurred, they are considered the most interesting from an operational perspective, as in the most of the applications no multitemporal ground truth information is available. Considering the above mentioned limitations, in this thesis we study the main problems related to multitemporal VHR images with particular attention to registration noise (i.e. the noise related to a non-perfect alignment of the multitemporal images under investigation). Then, on the basis of the results of the conducted analysis, we develop robust unsupervised and automatic change-detection methods. In particular, the following specific issues are addressed in this work: 1. Analysis of the effects of registration noise in multitemporal VHR images and definition of a method for the estimation of the distribution of such kind of noise useful for defining: a. Change-detection techniques robust to registration noise (RN); the proposed techniques are able to significantly reduce the false alarm rate due to RN that is raised by the standard CD techniques when dealing with VHR images. b. Effective registration methods; the proposed strategies are based on a multiscale analysis of the scene which allows one to extract accurate control points for the registration of VHR images. 2. Detection and discrimination of multiple changes in multitemporal images; this techniques allow one to overcome the limitation of the existing unsupervised techniques, as they are able to identify and separate different kinds of change without any prior information on the study areas. 3. Pre-processing techniques for optimizing change detection on VHR images; in particular, in this context we evaluate the impact of: a. Image transformation techniques on the results of the CD process; b. Different strategies of image pansharpening applied to the original multitemporal images on the results of the CD process. For each of the above mentioned topic an analysis of the state of the art is carried out, the limitations of existing methods are pointed out and the proposed solutions to the addressed problems are described in details. Finally, experimental results conducted on both simulated and real data are reported in order to show and confirm the validity of all the proposed methods

Unsupervised deep joint segmentation of multi-temporal high resolution images

High/very-high-resolution (HR/VHR) multitemporal images are important in remote sensing to monitor the dynamics of the Earth's surface. Unsupervised object-based image analysis provides an effective solution to analyze such images. Image semantic segmentation assigns pixel labels from meaningful object groups and has been extensively studied in the context of single-image analysis, however not explored for multitemporal one. In this article, we propose to extend supervised semantic segmentation to the unsupervised joint semantic segmentation of multitemporal images. We propose a novel method that processes multitemporal images by separately feeding to a deep network comprising of trainable convolutional layers. The training process does not involve any external label, and segmentation labels are obtained from the argmax classification of the final layer. A novel loss function is used to detect object segments from individual images as well as establish a correspondence between distinct multitemporal segments. Multitemporal semantic labels and weights of the trainable layers are jointly optimized in iterations. We tested the method on three different HR/VHR data sets from Munich, Paris, and Trento, which shows the method to be effective. We further extended the proposed joint segmentation method for change detection (CD) and tested on a VHR multisensor data set from Trento

Multisource and Multitemporal Data Fusion in Remote Sensing

The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary datasets, however, opens up the possibility of utilizing multimodal datasets in a joint manner to further improve the performance of the processing approaches with respect to the application at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several spaceborne sensors, the integration of the temporal information with the spatial and/or spectral/backscattering information of the remotely sensed data is possible and helps to move from a representation of 2D/3D data to 4D data structures, where the time variable adds new information as well as challenges for the information extraction algorithms. There are a huge number of research works dedicated to multisource and multitemporal data fusion, but the methods for the fusion of different modalities have expanded in different paths according to each research community. This paper brings together the advances of multisource and multitemporal data fusion approaches with respect to different research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references

Advanced Techniques based on Mathematical Morphology for the Analysis of Remote Sensing Images

Remote sensing optical images of very high geometrical resolution can provide a precise and detailed representation of the surveyed scene. Thus, the spatial information contained in these images is fundamental for any application requiring the analysis of the image. However, modeling the spatial information is not a trivial task. We addressed this problem by using operators defined in the mathematical morphology framework in order to extract spatial features from the image. In this thesis novel techniques based on mathematical morphology are presented and investigated for the analysis of remote sensing optical images addressing different applications. Attribute Profiles (APs) are proposed as a novel generalization based on attribute filters of the Morphological Profile operator. Attribute filters are connected operators which can process an image by removing flat zones according to a given criterion. They are flexible operators since they can transform an image according to many different attributes (e.g., geometrical, textural and spectral). Furthermore, Extended Attribute Profiles (EAPs), a generalization of APs, are presented for the analysis of hyperspectral images. The EAPs are employed for including spatial features in the thematic classification of hyperspectral images. Two techniques dealing with EAPs and dimensionality reduction transformations are proposed and applied in image classification. In greater detail, one of the techniques is based on Independent Component Analysis and the other one deals with feature extraction techniques. Moreover, a technique based on APs for extracting features for the detection of buildings in a scene is investigated. Approaches that process an image by considering both bright and dark components of a scene are investigated. In particular, the effect of applying attribute filters in an alternating sequential setting is investigated. Furthermore, the concept of Self-Dual Attribute Profile (SDAP) is introduced. SDAPs are APs built on an inclusion tree instead of a min- and max-tree, providing an operator that performs a multilevel filtering of both the bright and dark components of an image. Techniques developed for applications different from image classification are also considered. In greater detail, a general approach for image simplification based on attribute filters is proposed. Finally, two change detection techniques are developed. The experimental analysis performed with the novel techniques developed in this thesis demonstrates an improvement in terms of accuracies in different fields of application when compared to other state of the art methods

Multisource and multitemporal data fusion in remote sensing:A comprehensive review of the state of the art

The recent, sharp increase in the availability of data captured by different sensors, combined with their considerable heterogeneity, poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary data sets, however, opens up the possibility of utilizing multimodal data sets in a joint manner to further improve the performance of the processing approaches with respect to applications at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several

Automatic near real-time flood detection in high resolution X-band synthetic aperture radar satellite data using context-based classification on irregular graphs

This thesis is an outcome of the project “Flood and damage assessment using very high resolution SAR data” (SAR-HQ), which is embedded in the interdisciplinary oriented RIMAX (Risk Management of Extreme Flood Events) programme, funded by the Federal Ministry of Education and Research (BMBF). It comprises the results of three scientific papers on automatic near real-time flood detection in high resolution X-band synthetic aperture radar (SAR) satellite data for operational rapid mapping activities in terms of disaster and crisis-management support. Flood situations seem to become more frequent and destructive in many regions of the world. A rising awareness of the availability of satellite based cartographic information has led to an increase in requests to corresponding mapping services to support civil-protection and relief organizations with disaster-related mapping and analysis activities. Due to the rising number of satellite systems with high revisit frequencies, a strengthened pool of SAR data is available during operational flood mapping activities. This offers the possibility to observe the whole extent of even large-scale flood events and their spatio-temporal evolution, but also calls for computationally efficient and automatic flood detection methods, which should drastically reduce the user input required by an active image interpreter. This thesis provides solutions for the near real-time derivation of detailed flood parameters such as flood extent, flood-related backscatter changes as well as flood classification probabilities from the new generation of high resolution X-band SAR satellite imagery in a completely unsupervised way. These data are, in comparison to images from conventional medium-resolution SAR sensors, characterized by an increased intra-class and decreased inter-class variability due to the reduced mixed pixel phenomenon. This problem is addressed by utilizing multi-contextual models on irregular hierarchical graphs, which consider that semantic image information is less represented in single pixels but in homogeneous image objects and their mutual relation. A hybrid Markov random field (MRF) model is developed, which integrates scale-dependent as well as spatio-temporal contextual information into the classification process by combining hierarchical causal Markov image modeling on automatically generated irregular hierarchical graphs with noncausal Markov modeling related to planar MRFs. This model is initialized in an unsupervised manner by an automatic tile-based thresholding approach, which solves the flood detection problem in large-size SAR data with small a priori class probabilities by statistical parameterization of local bi-modal class-conditional density functions in a time efficient manner. Experiments performed on TerraSAR-X StripMap data of Southwest England and ScanSAR data of north-eastern Namibia during large-scale flooding show the effectiveness of the proposed methods in terms of classification accuracy, computational performance, and transferability. It is further demonstrated that hierarchical causal Markov models such as hierarchical maximum a posteriori (HMAP) and hierarchical marginal posterior mode (HMPM) estimation can be effectively used for modeling the inter-spatial context of X-band SAR data in terms of flood and change detection purposes. Although the HMPM estimator is computationally more demanding than the HMAP estimator, it is found to be more suitable in terms of classification accuracy. Further, it offers the possibility to compute marginal posterior entropy-based confidence maps, which are used for the generation of flood possibility maps that express that the uncertainty in labeling of each image element. The supplementary integration of intra-spatial and, optionally, temporal contextual information into the Markov model results in a reduction of classification errors. It is observed that the application of the hybrid multi-contextual Markov model on irregular graphs is able to enhance classification results in comparison to modeling on regular structures of quadtrees, which is the hierarchical representation of images usually used in MRF-based image analysis. X-band SAR systems are generally not suited for detecting flooding under dense vegetation canopies such as forests due to the low capability of the X-band signal to penetrate into media. Within this thesis a method is proposed for the automatic derivation of flood areas beneath shrubs and grasses from TerraSAR-X data. Furthermore, an approach is developed, which combines high resolution topographic information with multi-scale image segmentation to enhance the mapping accuracy in areas consisting of flooded vegetation and anthropogenic objects as well as to remove non-water look-alike areas

Deep Learning based data-fusion methods for remote sensing applications

In the last years, an increasing number of remote sensing sensors have been launched to orbit around the Earth, with a continuously growing production of massive data, that are useful for a large number of monitoring applications, especially for the monitoring task. Despite modern optical sensors provide rich spectral information about Earth's surface, at very high resolution, they are weather-sensitive. On the other hand, SAR images are always available also in presence of clouds and are almost weather-insensitive, as well as daynight available, but they do not provide a rich spectral information and are severely affected by speckle "noise" that make difficult the information extraction. For the above reasons it is worth and challenging to fuse data provided by different sources and/or acquired at different times, in order to leverage on their diversity and complementarity to retrieve the target information. Motivated by the success of the employment of Deep Learning methods in many image processing tasks, in this thesis it has been faced different typical remote sensing data-fusion problems by means of suitably designed Convolutional Neural Networks

A CNN-based fusion method for feature extraction from sentinel data

Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR) images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account—optical sequences, SAR sequences, digital elevation model—so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May–November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators

An Embedded Marked Point Process Framework for Three-Level Object Population Analysis

In this paper we introduce a probabilistic approach for extracting complex hierarchical object structures from digital images used by various vision applications. The proposed framework extends conventional Marked Point Process (MPP) models by (i) admitting object-subobject ensembles in parent-child relationships and (ii) allowing corresponding objects to form coherent object groups, by a Bayesian segmentation of the population. Different from earlier, highly domain specific attempts on MPP generalization, the proposed model is defined at an abstract level, providing clear interfaces for applications in various domains. We also introduce a global optimization process for the multi-layer framework for finding optimal entity configurations, considering the observed data, prior knowledge, and interactions between the neighboring and the hierarchically related objects. The proposed method is demonstrated in three different application areas: built in area analysis in remotely sensed images, traffic monitoring on airborne and mobile laser scanning (Lidar) data and optical circuit inspection. A new benchmark database is published for the three test cases, and the model's performance is quantitatively evaluated